The Two Faces of Algorithmic Management in the Gig Economy

Abstract

Algorithmic management of workers is a relatively new phenomenon which impacts workers in diverse manners. The growing literature on this disruptive and technology-mediated form of management suggest that, through different mechanisms, it can result in both beneficial and harmful consequences. Aiming to examine these two faces empirically and simultaneously, time-lagged data was collected from 366 gig workers. The results show that, on the one hand, high perceived exposure to AM is associated to greater perceived procedural justice. On the other hand, workers reporting high AM exposure also perceive lower job autonomy. This has the simultaneous effect of indirectly fostering and worsening the level of gig workers' engagement.